We design new differentially private algorithms for the Euclidean k-means problem, both in the centralized model and in the local model of differential privacy. In both models, our algorithms achieve significantly improved error guarantees than the previous state-of-the-art. In addition, in the local model, our algorithm significantly reduces the number of interaction rounds.
Although the problem has been widely studied in the context of differential privacy, all of the existing constructions achieve only super constant approximation factors. We present, for the first time, efficient private algorithms for the problem with constant multiplicative error. Furthermore, we show how to modify our algorithms so they compute private coresets for k-means clustering in both models.
Uri Stemmer (Ben-Gurion University)
Related Events (a corresponding poster, oral, or spotlight)
2018 Poster: Differentially Private k-Means with Constant Multiplicative Error »
Tue Dec 4th through Wed the 5th Room Room 517 AB
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